Results 11 to 20 of about 1,655 (147)

Probabilistic Inference in Credal Networks: New Complexity Results [PDF]

Journal of Artificial Intelligence Research, 2014
Credal networks are graph-based statistical models whose parameters take values in a set, instead of being sharply specified as in traditional statistical models (e.g., Bayesian networks). The computational complexity of inferences on such models depends on the irrelevance/independence concept adopted.
Mauá, D. D., de Campos, C. P., Benavoli, A., Antonucci, A.   +3 more
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Credal Sets Approximation by Lower Probabilities: Application to Credal Networks [PDF]

, 2010
Credal sets are closed convex sets of probability mass functions. The lower probabilities specified by a credal set for each element of the power set can be used as constraints defining a second credal set. This simple procedure produces an outer approximation, with a bounded number of extreme points, for general credal sets.
Alessandro Antonucci, Fabio Cuzzolin
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Pseudo Credal Networks for Inference With Probability Intervals [PDF]

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2019
Abstract The computation of the inference corresponds to an NP-hard problem even for a single connected credal network. The novel concept of pseudo networks is proposed as an alternative to reduce the computational cost of probabilistic inference in credal networks and overcome the computational cost of existing methods.
Estrada-Lugo, Hector Diego, Tolo, Silvia, De Angelis, Marco, Patelli, Edoardo   +3 more
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Recent advances in imprecise-probabilistic graphical models [PDF]

, 2012
We summarise and provide pointers to recent advances in inference and identification for specific types of probabilistic graphical models using imprecise probabilities.
De Bock, Jasper, De Cooman, Gert, Van Camp, Arthur   +2 more
core   +2 more sources

Belief Approach for Social Networks [PDF]

, 2014
Nowadays, social networks became essential in information exchange between individuals. Indeed, as users of these networks, we can send messages to other people according to the links connecting us. Moreover, given the large volume of exchanged messages,
Dhaou, Salma Ben, Kharoune, Mouloud, Martin, Arnaud, Yaghlane, Boutheina Ben   +3 more
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Credal networks

Artificial Intelligence, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Valuation Network for Ongoing Assessment of Threat to an Underwater Vehicle

IEEE Access
The paper develops a valuation based system for reasoning under uncertainty in the context of threat assessment onboard an underwater vehicle. The focus is on threat posed by the nearby contacts, while the vessel is navigating busy waters with warships ...
Branko Ristic, Amanda Bessell, Sanjeev Arulampalam   +2 more
doaj   +1 more source

Research on Synchronous Transfer Control Technology for Distribution Network Load Based on Imprecise Probability

Mathematics
As the penetration rate of distributed power sources increases and distribution network structures grow increasingly complex, the uncertainty in switch action control during load transfer has become a critical issue affecting grid safety and reliability.
Hua Zhang, Cheng Long, Xueneng Su, Yiwen Gao, Wei Luo   +4 more
doaj   +1 more source

Multiple Imputation Ensembles (MIE) for dealing with missing data [PDF]

, 2020
Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation ...
A Farhangfar, AM Sefidian, B Schölkopf, C Cortes, CT Tran, DA Newman, DB Rubin, DB Rubin, DH Wolpert, EL Silva-Ramírez, GE Batista, GJ van der Heijden, H Gao, IH Witten, J Demšar, J Honaker, J Honaker, J Scheffer, JA Sterne, JL Schafer, JL Schafer, JR Quinlan, K Abayomi, KM Ting, L Breiman, L Breiman, L Rokach, M Fichman, M Khalilia, M Spratt, MA Klebanoff, MJ Azur, NJ Horton, PJ García-Laencina, PJ Kelly, PN Tan, RJ Little, S García, S Van Buuren, S Van Buuren, SS Chae, SS Choi, U Garciarena, V Vapnik, X Chen, Y Dong, Y Freund, Y He, Z Che, Z Liu   +49 more
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Credal networks for military identification problems

International Journal of Approximate Reasoning, 2009
AbstractCredal networks are imprecise probabilistic graphical models generalizing Bayesian networks to convex sets of probability mass functions. This makes credal networks particularly suited to model expert knowledge under very general conditions, including states of qualitative and incomplete knowledge. In this paper, we present a credal network for
Antonucci, Alessandro, Brühlmann, Ralph, Piatti, Alberto, Zaffalon, Marco   +3 more
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